EXPERIMENTAL AND THERAPEUTIC MEDICINE 13: 2793-2800, 2017
Alteration of stomach microbiota compositions in the progression of gastritis induces nitric oxide in gastric cell TIANYI DONG1,2, QIANG FENG3, FENGYAN LIU4, LAP KAM CHANG1, XIANGYU ZHOU1, MINGYONG HAN2, XINGSONG TIAN2, NING ZHONG4 and SHILI LIU1 1
Department of Medical Microbiology, School of Medicine, Shandong University, Jinan, Shandong 250012; 2Department of Breast Thyroid Surgery, Shandong Provincial Hospital, Shandong University; 3Department of Human Microbiome, School of Stomatology, Shandong University, Shandong Provincial Key Laboratory of Oral Tissue Regeneration, Jinan, Shandong 250021; 4Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, P.R. China Received November 21, 2016; Accepted March 29, 2017 DOI: 10.3892/etm.2017.4373 Abstract. Atrophic gastritis is considered to be an antecedent to intestinal metaplasia and gastric cancer. A previous study identified that Helicobacter pylori was absent at the severe atrophic gastritis stage, and alterations in the gastric microbial composition resembled those in gastric cancer. To explore the role of the bacteria absence of H. pylori in gastric carcinogenesis, in the current study, we compared the microbiota of clinically collected H. pylori‑free gastric fluids from 30 patients with non‑atrophic gastritis (N) and 22 patients with severe atrophic gastritis (S). We estimated the bacterial loads in the N and S groups by colony counting in culture agar as well as by measuring the concentration of the extracted DNA. The results showed a significant increase in bacterial load in patients with atrophic gastritis in comparison to non‑atrophic gastritis. Then, we analyzed the microbial communities of the gastric fluids from all 52 patients using high‑throughput sequencing of 16S rRNA amplicons. The Chao 1, Shannon and Simpson diversity indexes demonstrated that the bacterial richness and diversity were not significantly different between the N and S groups. Moreover, principal component analysis illustrated that the microbiomes from the S group were more scattered. Microbiota composition analysis showed that the entire dataset was clustered into 27 phyla, 61 classes, 106 orders, 177 families, 292 genera and 121 species. At the genus level, only the abundance of Prevotella was significantly different
Correspondence to: Dr Ning Zhong, Department of Gastro enterology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, Shandong 250012, P.R. China E‑mail:
[email protected]
Dr Shili Liu, Department of Medical Microbiology, School of Medicine, Shandong University, 44 Wenhuaxi Road, Jinan, Shandong 250012, P.R. China E‑mail:
[email protected]
Key words: gastritis, microbiota, prevotella, nitric oxide
between the N and S groups. Further analysis showed that all the higher taxonomic categories were significantly different between the N and S groups. To assess the effects of the metabolic products of Prevotella spp. on gastric cell physiology, we treated the human gastric epithelial cell line AGS with acetic acid and monitored nitric oxide (NO) production. The results showed that acetic acid at low concentrations (0.5 and 5 µM) significantly inhibited AGS cells to secrete NO compared to phosphate buffer saline‑treated control cells. These results suggest that the microbiota in non‑atrophic gastritis may influence gastric epithelial cell physiology. Introduction Gastric cancer is one of the most common malignant tumors. It accounts for about 10% of all invasive cancers and is the second leading cause of cancer deaths worldwide (1). Although the incidence of gastric cancer in the west had declined, it remains the most common type of cancer in Asia. In China, the total number of cases and deaths from gastric cancer have increased concomitant with huge ongoing demographic changes (2,3), resulting in an urgent need to find more effective diagnostic methods and treatments. The human stomach harbors a large number of bacteria including Helicobacter pylori (4,5); these microbial organisms collectively compose the microbial community called the microbiota. Previous studies have shown that bacterial factors play an important role in the development of gastric cancer (5,6). Gastric cancer develops from a multifactorial, multistep inflammatory process, progressing through the stages of superficial gastritis, atrophic gastritis, intestinal metaplasia, and dysplasia before the development of gastric cancer (7). At the atrophic gastritis stage, the secretion of essential substances such as hydrochloric acid, pepsin, and intrinsic factor are impaired, resulting in a neutralized stomach environment. In turn, many other bacterial species can colonize the new environment leading to gastric microbiota overgrowth as the gastritis progress and low acid production (8,9). Atrophic gastritis is considered to be an antecedent to intestinal metaplasia and gastric cancer (10,11), thus, exploring the bacterial
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factors that propel the progress of atrophic gastritis may be helpful for elucidating the mechanisms underlying gastric cancer development. In previous studies, infection with H. pylori was reported to closely correlate with the other bacteria present in stomach (12), and clinical observations have found that atrophic gastritis without H. pylori is more likely to develop into gastric cancer than cases with H. pylori, an observation that has been supported by animal studies. Lee et al also showed that intervention with antimicrobial therapies delayed the onset of gastric cancer in transgenic mice irrespective of H. pylori infection status (8). Moreover, other research has shown that feeding germ‑free transgenic mice with artificial microbiota accelerated the occurrence of cancer (6). Noticeably, a study by Engstrand and Lindberg found that the changes in the microbiota in individuals with atrophic gastritis resemble those seen in gastric cancer (5). Thus, the absence of H. pylori in atrophic gastritis may play a crucial role in gastric carcinogenesis; thus, the alterations in the composition of gastric microbiota and its interaction with host cells require further studies. However, there has been little study on the characterization of the absence of H. pylori in the progress of gastritis. In the current study, we investigated the variation in gastric microbiota using gastric fluids from 30 patients with non‑atrophic gastritis (N) and 22 patients with chronic atrophic gastritis (S). Our results suggest that the microbiota in gastric fluids might influence gastric epithelial cell physiology and gastric carcinogenesis. Materials and methods Patient samples collection and cell culture. Gastric fluids newly extracted from 30 non‑atrophic gastritis and 22 atrophic gastritis patients by endoscopy were obtained from Qilu Hospital of Shandong University (Jinan, China). All patients gave their informed consent prior to their inclusion in the study. The gastric fluids were kept at ‑80˚C and used for subsequent analysis. All studies were reviewed and approved by the Ethic Committee of Shandong University (Jinan, China). Gastric adenocarcinoma cell line AGS cells were maintained in our laboratory. AGS cells were cultured in Ham's F‑12 medium (HyClone, Logan, UT, USA) supplemented with 10% FCS and 1% penicillin‑streptomycin. BGC‑823 cells were cultured in RPMI‑1640 (Life Technologies, Carlsbad, CA, USA) supplemented with 10% FCS (Tianhang Co., Ltd., Hangzhou, China) and 1% penicillin‑streptomycin. Sequencing method Extraction of genome DNA. Total genome DNA from samples was extracted using CTAB/SDS method. DNA concentration and purity was monitored on 1% agarose gels. According to the concentration, DNA was diluted to 1 ng/µl using sterile water. Amplicon generation. Primer: 16S V4: 515F‑806R, 18S V4: 528F‑706R, 18S V9: 1380F‑1510R, ITS1: ITS1FITS2. 16S/18S rRNA genes were amplified used the specific primer with the barcode. All PCR reactions were carried out in 30 µl reactions with 15 µl of Phusion® High‑Fidelity PCR Master
Mix (New England Biolabs, Ipswich, MA, USA); 0.2 µM of forward and reverse primers, and about 10 ng templates DNA. Thermal cycling consisted of initial denaturation at 98˚C for 1 min, followed by 30 cycles of denaturation at 98˚C for 10 sec, annealing at 50˚C for 30 sec, and elongation at 72˚C for 30 sec. Finally 72˚C for 5 min. PCR products quantification and qualification. Mix same volume of 1X loading buffer (contained SYBR-Green) with PCR products and operate electrophoresis on 2% agarose gel for detection. Samples with bright main strip between 400‑450 bp were chosen for further experiments. PCR products mixing and purification. PCR products were mixed in equidensity ratios. Then, mixture PCR products was purified with GeneJET Gel Extraction Kit (Thermo Scientific). Library preparation and sequencing. Sequencing libraries were generated using NEB Next® Ultra™ DNA Library Prep kit for Illumina (New England Biolabs) following manufacturer's recommendations and index codes were added. The library quality was assessed on the
[email protected] Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system. At last, the library was sequenced on an Illumina MiSeq platform and 250/300 bp paired‑end reads were generated. Data analysis. Paired‑end reads from the original DNA fragments are merged by using FLASH‑a very fast and accurate analysis tool which is designed to merge paired‑end reads when there are overlaps between reads1 and reads2. Paired‑end reads was assigned to each sample according to the unique barcodes. Sequences were analyzed using QIIME software package (Quantitative Insights Into Microbial Ecology), and in‑house Perl scripts were used to analyze alpha‑(within samples) and beta‑(among samples) diversity. First, reads were filtered by QIIME quality filters. Then we use pick_de_novo_otus.py to pick operational taxonomic units (OTUs) by making OTU table. Sequences with ≥97% similarity were assigned to the same OTUs. We pick a representative sequences for each OTU and use the RDP classifier to annotate taxonomic information for each representative sequence. In order to compute Alpha Divesity, we rarify the OTU table and calculate three metrics: Chao1 estimates the species abundance; Observed Species is estimates the amount of unique OTUs found in each sample, and Shannon index. Rarefaction curves were generated based on these three metrics. QIIME calculates both weighted and unweighted unifrac, which are phylogenetic measures of beta diversity. RNA extraction and quantitative real‑time PCR (QRT‑PCR). Total cellular RNA was extracted with TRIzol (Life Technologies) according to the protocol provided by the manufacturer. First‑strand cDNA was synthesized from 1 µg total cellular or tissue RNA using the RevertAid™ First Strand cDNA Synthesis kit (Thermo Fisher Scientific, Waltham, MA, USA) with random primers. Then cDNA was amplified for quantitative real‑time PCR, the specific primers used were as follows: for human nitric oxide synthase 2 (NOS2) forward primer 5'‑GTTC TCA AGG CACAGGTCTC‑3' and reverse primer 5'‑GCAG GTCACT TATGTCACT TATC‑3';
EXPERIMENTAL AND THERAPEUTIC MEDICINE 13: 2793-2800, 2017
for β ‑actin, forward primer 5'‑AGTTGCGTTACACCCT TT CTTG‑3' and reverse primer 5'‑CACCTTCACCGTTCCAGT TTT‑3'. The real‑time PCR reactions were performed at: 95˚C, 10 sec (denaturation); 55˚C, 30 sec (annealing); 72˚C, 30 sec (extension) for 35 cycles. The real‑time PCR reactions were performed on the ABI7000 Fast Real‑Time PCR System with SYBR Premix Ex Taq™ according to the procedures. Acetic acid treatment and NO detection. Cells were seeded in 6‑well plates and treated with different concentrations of acetic acid for 72 h. NO was detected with Nitric Oxide Colorimetric Assay kit (Amersco, LLC, Solon, OH, USA) according to procedures the manual instructed. Statistical analyses. All experiments were repeated at least three times and the data were expressed as mean ± standard deviation (SD). The differences between the three groups were compared using the Student's t‑test and P
